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   "source": [
    "# The plot module: visualizing grain size distributions\n",
    "\n",
    "The plot module includes a series of plots to visualize and characterize grain size populations. Before we get into the details, let's run the GrainSizeTools script and load the example dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "module plot imported\nmodule averages imported\nmodule stereology imported\nmodule piezometers imported\nmodule template imported\n\n======================================================================================\nWelcome to GrainSizeTools script\n======================================================================================\nA free open-source cross-platform script to visualize and characterize grain size\npopulation and estimate differential stress via paleopizometers.\n\nVersion: v3.0.2 (2020-12-30)\nDocumentation: https://marcoalopez.github.io/GrainSizeTools/\n\nType get.functions_list() to get a list of the main methods\n\n"
     ]
    }
   ],
   "source": [
    "# Load the script first (change the path to GrainSizeTools_script.py accordingly!)\n",
    "%run C:/Users/marco/Documents/GitHub/GrainSizeTools/grain_size_tools/GrainSizeTools_script.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               Area  Circ.    Feret  FeretX  FeretY  FeretAngle  MinFeret  \\\n",
       "0        1   157.25  0.680   18.062  1535.0     0.5     131.634    13.500   \n",
       "1        2  2059.75  0.771   62.097   753.5    16.5     165.069    46.697   \n",
       "2        3  1961.50  0.842   57.871   727.0    65.0      71.878    46.923   \n",
       "3        4  5428.50  0.709  114.657  1494.5    83.5      19.620    63.449   \n",
       "4        5   374.00  0.699   29.262  2328.0    34.0      33.147    16.000   \n",
       "...    ...      ...    ...      ...     ...     ...         ...       ...   \n",
       "2656  2657   452.50  0.789   28.504  1368.0  1565.5     127.875    22.500   \n",
       "2657  2658  1081.25  0.756   47.909  1349.5  1569.5     108.246    31.363   \n",
       "2658  2659   513.50  0.720   32.962  1373.0  1586.0     112.286    20.496   \n",
       "2659  2660   277.75  0.627   29.436  1316.0  1601.5     159.102    17.002   \n",
       "2660  2661   725.00  0.748   39.437  1335.5  1615.5     129.341    28.025   \n",
       "\n",
       "         AR  Round  Solidity  diameters  \n",
       "0     1.101  0.908     0.937  14.149803  \n",
       "1     1.314  0.761     0.972  51.210889  \n",
       "2     1.139  0.878     0.972  49.974587  \n",
       "3     1.896  0.528     0.947  83.137121  \n",
       "4     1.515  0.660     0.970  21.821815  \n",
       "...     ...    ...       ...        ...  \n",
       "2656  1.235  0.810     0.960  24.002935  \n",
       "2657  1.446  0.692     0.960  37.103777  \n",
       "2658  1.493  0.670     0.953  25.569679  \n",
       "2659  1.727  0.579     0.920  18.805379  \n",
       "2660  1.351  0.740     0.960  30.382539  \n",
       "\n",
       "[2661 rows x 12 columns]"
      ],
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     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "# Load the example dataset\n",
    "filepath = 'C:/Users/marco/Documents/GitHub/GrainSizeTools/grain_size_tools/DATA/data_set.txt'\n",
    "dataset = pd.read_csv(filepath, sep='\\t')  # Import the example dataset\n",
    "dataset['diameters'] = 2 * np.sqrt(dataset['Area'] / np.pi)  # estimate ECD\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All methods of the *plot* module can be invoked by writing ```plot.*```, where * refers to the plot to be used.\n",
    "\n",
    "> 👉 If you write ``plot.`` and then hit the tab key a menu will pop up with all the methods implemented in the module\n",
    "\n",
    "The main method is ```plot.distribution()```. The method allows to visualize the grain size population using the histogram and/or the kernel density estimate (KDE) and provides the location of the different averages. The simplest example of use would be to pass the column with the diameters as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "=======================================\nNumber of classes =  45\nbinsize =  3.41\n=======================================\n=======================================\nKDE bandwidth =  4.01\n=======================================\n"
     ]
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "fig, ax = plot.distribution(dataset['diameters'])  # this is just to show the plot in the notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the ```plot.distribution()``` function returns a plot containing the histogram and the kernel density values of the distribution and the location of the averages estimated by the function ``summarize`` by default."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 👉 Note that when calling ``plot.distribution()`` we store two variables ``fig`` and ``ax``. The variable ``fig`` stems for figure and ``ax`` for the axe to the current figure*. This is not compulsory. Indeed, you can directly write ``plot.distribution(dataset['diameters'])`` to get same result. However, if you want to save or tweak the plot later you will need the ``fig`` and ``ax`` variables (or any name you prefer). The variable ``fig``, for example, allows you to save the plot (see an example below).\n",
    ">\n",
    "> *If you can't tell the difference between a figure and an axe check the elements of a figure here: https://matplotlib.org/3.2.1/gallery/showcase/anatomy.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you want to save the plot in your hard disk\n",
    "fig.savefig(\"test_distribution.png\", dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the format of image (e.g. 'png', 'pdf', 'tif', 'svg') will depend on the extension of the file (after the dot) you define.\n",
    "Some interesting parameters when saving a figure are:\n",
    "- ``dpi`` : the resolution in dots per inch\n",
    "- ``facecolor`` : the facecolor of the figure, e.g. 'white', 'black', etc.\n",
    "- ``quality`` : the image quality from 1 (worst) to 95 (best). Only applicable if format is *jpg*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "\u001b[1;31mSignature:\u001b[0m\n",
      "\u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistribution\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mplot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hist'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'kde'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mavg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'amean'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'gmean'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'median'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'mode'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mbinsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'auto'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mbandwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'silverman'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[1;33m**\u001b[0m\u001b[0mfig_kw\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mDocstring:\u001b[0m\n",
      "Return a plot with the ditribution of (apparent or actual) grain sizes\n",
      "in a dataset.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "data : array_like\n",
      "    the size of the grains\n",
      "\n",
      "plot : string, tuple or list; optional\n",
      "    the type of plot, either histogram ('hist'), kernel density estimate\n",
      "    ('kde') or both ('hist', 'kde'). Default is both.\n",
      "\n",
      "avg : string, tuple or list; optional\n",
      "    the central tendency measures o show, either the arithmetic ('amean')\n",
      "    or geometric ('gmean') means, the median ('median'), and/or the\n",
      "    KDE-based mode ('mode'). Default all averages.\n",
      "\n",
      "binsize : string or positive scalar; optional\n",
      "    If 'auto', it defines the plug-in method to calculate the bin size.\n",
      "    When integer or float, it directly specifies the bin size.\n",
      "    Default: the 'auto' method.\n",
      "\n",
      "    | Available plug-in methods:\n",
      "    | 'auto' (fd if sample_size > 1000 or Sturges otherwise)\n",
      "    | 'doane' (Doane's rule)\n",
      "    | 'fd' (Freedman-Diaconis rule)\n",
      "    | 'rice' (Rice's rule)\n",
      "    | 'scott' (Scott rule)\n",
      "    | 'sqrt' (square-root rule)\n",
      "    | 'sturges' (Sturge's rule)\n",
      "\n",
      "bandwidth : string {'silverman' or 'scott'} or positive scalar; optional\n",
      "    the method to estimate the bandwidth or a scalar directly defining the\n",
      "    bandwidth. It uses the Silverman plug-in method by default.\n",
      "\n",
      "**fig_kw :\n",
      "    additional keyword arguments to control the size (figsize) and\n",
      "    resolution (dpi) of the plot. Default figsize is (6.4, 4.8).\n",
      "    Default resolution is 100 dpi.\n",
      "\n",
      "Call functions\n",
      "--------------\n",
      "- gaussian_kde (from Scipy stats)\n",
      "\n",
      "Examples\n",
      "--------\n",
      ">>> distribution(data['diameters'])\n",
      ">>> distribution(data['diameters'], figsize=(6.4, 4.8))\n",
      "\n",
      "Returns\n",
      "-------\n",
      "A plot showing the distribution of (apparent) grain sizes and\n",
      "the location of the averages defined.\n",
      "\u001b[1;31mFile:\u001b[0m      c:\\users\\marco\\documents\\github\\grainsizetools\\grain_size_tools\\plot.py\n",
      "\u001b[1;31mType:\u001b[0m      function\n"
     ],
     "name": "stdout"
    }
   ],
   "source": [
    "plot.distribution?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">TODO:\n",
    ">- Explain all parameters\n",
    ">- Examples of histogram vs KDE\n",
    ">- Example of a log-transformed distribution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing lognormality\n",
    "\n",
    "Sometimes can be helpful to test whether the data follows or deviates from a lognormal distribution. For example, to find out if the dataset is suitable for applying the two-step stereological method or which confidence interval method is best. The script uses two methods to test whether the distribution of grain size follows a lognormal distribution. One is a visual method named [quantile-quantile (q-q) plots]([https://en.wikipedia.org/wiki/Q%E2%80%93Q_plot](https://en.wikipedia.org/wiki/Q–Q_plot)) and the other is a quantitative test named the [Shapiro-Wilk test](https://en.wikipedia.org/wiki/Shapiro–Wilk_test). For this we use the GrainSizeTools function ```test_lognorm``` as follows :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "=======================================\nShapiro-Wilk test (lognormal):\n0.99, 0.02 (test statistic, p-value)\nIt doesnt look like a lognormal distribution (p-value < 0.05)\n(╯°□°）╯︵ ┻━┻\n=======================================\n"
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "fig2, ax = plot.qq_plot(dataset['diameters'], figsize=(6, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig2.savefig(\"qqplot.png\", dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Shapiro-Wilk test returns two different values, the test statistic and the p-value. This test considers the distribution to be lognormally distributed when the p-value is greater than 0.05. The q-q plot is a visual test that when the points fall right onto the reference line it means that the grain size values are lognormally distributed. The q-q plot has the advantage over the Shapiro-Wilk test that it shows where the distribution deviates from lognormality. \n",
    "\n",
    "> 👉 To know more about the q-q plot see https://serialmentor.com/dataviz/\n",
    "\n",
    "---\n",
    "\n",
    "## The area-weighted distribution\n",
    "\n",
    "The plot module also allows plotting the area-weighted distribution of grain sizes using the function ``area_weighted()``. This function also returns some basic statistics such as the area-weighted mean and the histogram features. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "=======================================\nDESCRIPTIVE STATISTICS\nArea-weighted mean grain size = 53.88 microns\n=======================================\nHISTOGRAM FEATURES\nThe modal interval is 40.85 - 44.26 microns\nThe number of classes are 46\nThe bin size is 3.40 according to the auto rule\n=======================================\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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RuHDhAvr16wdfX1/MnTsXEydOxIYNG+r9fS8OHEbIRcjdu3cjPDwcgwYNgqurKwYOHIhOnTrxtSsoKMCNGzdw7Ngx/PPPP5g3bx7c3d0lHjgbKSkpGDhwoLTDaJHu5qWLHIx0XUKE0GVF1Te0bql9AMw0TT4RtfTl5+fz3XeQBW/evJGJ+YDy8/Nx69YtODk5ce+llJaWQldXF7t378a4ceOkHGHDSesYNeb/vNBLcNOnT8eIESPw22+/YeHChXj37h06d+6Mjh07on379njz5g0KCwtRVFSEDh06wN3dHStXruSbQ4gQcag7qxKEhukhbJWUlMDFxQUBAQGYNm0aSkpK8PPPP6NDhw40pqUUiLwHVPeU7MKFC3Ht2jXcvHkTubm5KC0tRbdu3WBjY4PBgwfDysoKCgoKTRUzaUWeHL4LAHg9tEzk2RElIMJGjx49cPLkSSxbtgxbt26FiooKLC0tce7cOZrVWQpYjQWnqqoq1g4Jt27dQlhYGFJTU6GqqorRo0dj/vz5Tdr9j7QM+cnPpR0CkTFOTk5wcnKSdhgEnxgLThLu3bsHT09PdO7cGWvXrsWMGTMQExOD9evXN3UohBBCpIjVGZA4hYWFwczMDBs3buQ+S8AwDE6dOtXUoRBCCJGiJj0DKi0txfXr1+Hh4QF5eXnuU8DffPMNzxAWhBBCZF+TJqCMjAzU1NRATU0N06ZNg6mpKfr164fly5fzDFtOSEv24dD4hMiympqaTw4nJEqTXoIrLCwEAHz//fcYMWIEvLy8cP/+fWzZsgUVFRUCp15OSUlp1DYbu3xr9V5D8HhTwH+DGzakXhJ1paWlSMlsPsf51atX6Natm9Axu1qqN2/eSDsE8glNeYwYhsHLly/x+vVrZGZmCmzzqecwWSWgmpoaHDp0CAkJCXj1SvCUyVFRUZ9cT93OsbS05E7+ZGNjAw6Hg40bN2LBggV8zxE15kFSehBVNFFTYMsJGJWgzofjgNW3vr517XU6fHI5VVVVmH3WfB5Sff/+PV69eiVTCaiwsFDgg+ik+WjqY8QwDIyMjBo1nBCrBLR582Zs374dQ4YMadQvO1VVVQCAnZ0dT/nw4cOxbt06PHr0CFZWVg1aN6k/QVNg1wkaOlNgeVMz8xsq7RDqrW3bttDU1JR2GGL17Nkz9OrVS9phEBFa4jFilYCio6Mxc+ZMBAYGNmpjdZNRfXzNsO79p35ZE0IIkR2sOiFUVFSgX79+jd5Yjx490KVLF8THx/OUx8fHo23btujRo0ejt0EIIaRlYJWAzM3N8e+//zZ6Y/Ly8pg7dy7OnDmDJUuW4MyZM9i4cSN++eUXTJ06VeBQ76R1S150CsmL6BkxQmQRq0twy5Ytw+zZs6Grq4uRI0eiQ4cODd7gpEmTwDAMdu/ejbi4OO6Q7N7e3g1eJyGEkJaHVQKaMmUK3r17h++//x4//PCDwDYPHjxgvdH6zHtPCCFENrFKQIsWLZJ0HIQQQloZVgnoyy+/lHQchBBCWhnWIyGUlpZi165dSEhIQGFhITQ0NDBs2DBMnz6dOg8QQgipN1a94AoLCzFu3Dhs374dNTU1MDMzA4fDQUREBCZMmICysjJJx0kIIUTGsDoD2rBhA0pKSvDHH3/A3NycW3737l14eXlhzZo1WL58ucSCJK2X4ThTaYdACJEQVmdA58+fx6xZs3iSDwCYmZlh1qxZOHfunESCI0RzkD40B+lLOwxCiASwSkClpaXQ1dUVWKejo4PS0lKxBkUIIUT2sUpAhoaGuHjxosC6K1euQF+ffqESychLykRekuCh3gkhLRure0CTJk1CSEgIVFRUMHHiRHTr1g05OTk4ePAgYmJisHLlSknHSVqpp7H3al80bhxcQkgzxCoBTZ48Gc+ePUNUVBTfvD+enp4YN26cRIIjhBAiu1g/B7RkyRJ4eHjg2rVrePXqFTQ0NGBtbU2X3wghhDRIvabk1tfXp4RDCCFELIQmIE9PTyxevBh9+vSBp6fnJ1fEZkpuQgghpI7QBKSjowMFBQUAaNQ03IQQQoggQhPQqlWruK9DQ0ObJBhCCCGtB6vngDw9PXH//n2Bdffv32d1iY6QhrBaPRpWq0dLOwxCiASwSkBJSUlCRzt4/Pgxbt68KdagCCGEyD6hl+DCw8MRHh7OfT9t2jShK+nTp494oyKEECLzhCYgR0dH6OjogGEYLFmyBF5eXujRowdfu/bt22Pw4MESDZK0Xnc3J9S+GDpTuoEQQsROaALq1asXevXqBaD2bMjR0RH9+/dvssAIAYDyrNfSDoEQIiGsp2Po06cPLly4wC178uQJEhMTwTCMxIIjhBAiu1gloNzcXIwdOxb+/v7csgcPHuCbb76Bh4cHiouLJRQeIYQQWcUqAW3cuBHV1dU4cOAAt2z06NE4cOAAsrKysHHjRknFRwghREaxSkAJCQmYM2cOX2+3zz//HDNnzsSlS5ckEhwhhBDZxSoB1Y1+LUjnzp1RUFAg1qAIIYTIPlYJyMjICKdOnRJYd/nyZejp6Yk1KELqdLHSQxcr+v9FiCxiNR3DN998gyVLlqCqqgoTJkyAlpYWCgsLERcXh0OHDuH777+XdJyklTIabybtEAghEsIqAY0bNw4lJSXYvHkz/vzzTwAAwzBQUlKCn58fjQVHCCGk3lhPSDd9+nS4u7sjLS0N2dnZ6NSpE3r27Ak1NTVJxkdauTcvSqQdAiFEQuo1I6qSkhL69euHfv36cctu376NXbt2YfPmzeKOjRDc23Kt9sXX0o2DECJ+rBJQUVERlixZgsePH/OMfMAwDEpK6BcqIYSQ+mOVgEJCQpCUlAQHBwckJiaid+/e6NevH1JSUlBWVkZnP0Sq5DlyuJuXLrCuc3sNaKt0aeKICCFssEpA169fR2BgIDw9PXHw4EFcvHgRfn5+AABvb2/cuXOHRsQmUvP6XRnWJUQIrFtqH0AJiJBmitVzQOXl5TAyMgIAmJubIyMjg1vn4uKCmJgYyURHCCFEZrFKQNra2nj27BkAwNDQEFlZWSgrKwMAqKqqIicnR3IREkIIkUmsEpCLiwvCw8MRHR0NJSUlmJqaYuXKlbh37x6io6Ohra0t6TgJIYTIGFb3gHx8fJCXl4c9e/bA3d0dPj4+mDNnDmJjY8EwDJYvXy7pOEkrZepr3ajlRXVQAKiTAiHSxCoBtWnTBitWrOC+t7GxwfHjx3H//n0YGxvDxMREYgGS1k1Zt3EPOovqoABQJwVCpInVJThbW1ucO3eOp8zIyAhjxoyh5EMIIaRBWCUgPT09JCcnSzoWQvg8OXwXTw7flXYYhBAJYHUJbtGiRfDy8oKpqSnGjh0LDocj6biImOSU5aOg/JXAusrqyiaOpv7yk59LOwRCiISwSkC//PILunbtikWLFmHlypXo1q0b2rdvz9Pm999/l0iApHEKyl9h+YUNAuuChs5s4mgIIeQ/rBJQhw4d0KdPH74puQkhhJCGEpqALly4gD59+kBLSwt+fn7o1KkT2rVr15SxEUIIkWFCOyHMmzcPaWlpAAB7e3v8/fffTRYUIYQQ2Sf0DEhTUxMxMTEoLCwEwzC4fPkysrOzha7Izc1NEvERQgiRUUITUEBAAJYvX46//voLHA4HO3bsELoSDodDCYhIRHudDtIOgRAiIUIT0JgxYzBmzBgAQM+ePREZGSn2KRcYhoGnpyd0dHQQGhoq1nUT2WDmN1TaIRBCJITVg6i+vr7Q09MT+8YPHjyIpKQksa+XEEJI88eqG/bcuXPFvuHs7Gxs2bIFAwcOFPu6W5uW/rApIaR1YpWAJOGnn37Ct99+i4cPH0orBJkhyw+bJi86Vfviasv+HIQQfqwuwYlbbGwsioqK8M0330hj84QQQpqBJj8Dys/PR1hYGHbu3Al5efmm3jwhhJBmotEJKCcnB3fv3oWjoyOr9sHBwRg/fjzrYX1SUlIaE16jl28J3msIHxy2urq6Seuksc3GxFpaWoqUTNn/PyIOreFvqaVrbsfoU/f4WSegM2fO4PHjx2AYhlvGMAwePHiAy5cv486dO59cx+nTp5GamoqVK1eisrL25njdF0RlZSXatm3LN9J2YzoppKSktIpODqJm/BR1limJOmlsszGxqqqqwuwzmtPqU1rL31JL1hKPEasEtGPHDoSFhXGTg7q6OpSUlJCbmwsFBQWMHz+e1cZu3bqFFy9ewNLSkq/u2LFjOHbsGHr37l2P8AkhhLRUrBLQH3/8gS+++AIrVqzAmjVrUFVVhZCQEDx//hzffvst94HVT5k2bRpf261btwIAfHx8YGhoWL/oCSGEtFisElB2dja+//57KCgowNnZGWvWrAFQO1Pq5MmTsXHjRuzbt++T69HR0YGOjg5PWceOHQEAFhYW9Y2dtAKG40ylHQIhREJYJSBVVVVUVFQAAIyMjPDkyRMwDAMOhwNDQ0PcvUtTJhPJ0BykL+0QCCESwioBDRgwAAcOHIC1tTW6dOkCZWVlnD9/HiNGjEB6enqj5gmiMeAIIaR1YpWA/P394enpCVdXV1y8eBHjxo2Dn58funfvjoyMDIwePVrScZJWKi8ps/YFjUlKiMxhlYA+++wznDx5Ev/88w+A2sFJORwO7ty5g2HDhklkrDhCAOBp7L3aF4FNv21RY+x1bq8BbZUuTRwRIbKF9XNA6urqsLW1RVlZGbKzs+Ht7Y327dtLMjZCpErUGHtL7QMoARHSSKzHgrt16xYmT54MS0tLjB07Fo8ePcLs2bOxYYPgP1BCCCFEFFYJKCEhAVOnTkWbNm0QEhKCmpoaALWT1kVFRWHXrl0SDZIQQojsYZWA1q9fDxsbG0RFRfFMve3i4gIfHx/ExMRIKj5CCCEyilUCevjwIVxdXQXWffbZZ8jKyhJrUIQQQmQfq04I6urqyM3NFViXmZmJDh06iDUoQpo7eY6c0EFgqYccIeywSkAuLi7YunUrTE1NYW5uDgDgcDjIysrCrl274OzsLNEgSeuddttqdfN8xuz1uzKsS4gQWEc95Ahhh1UC8vPzQ1paGqZMmYKuXbsCAIKCgpCTk4MePXpgwYIFEg2SyPa024SQ1olVAlJUVMTOnTtx5swZnD17Ft27d4e2tjYGDhwIV1dXyMlJZWZvQgghLRirBBQWFgY3Nzc4OzvT5TbSpO5uTqh9QWd5hMgcVgno7Nmz0NTUhLGxsaTjIYRHedZraYdACJEQVtfOJk2ahMjISLx+TV8GhBBCxIPVGZC+vj60tbXh5OQEV1dX6OjoQFlZmacN22m5CSGEEIBlApozZw739Z49e/jqORwOJSBCCCH1wioBpaWlSToOQgghrUyj+0/fvn0b8+bNE0cshBBCWhFWZ0BFRUVYsmQJHj9+DIZhuOUMw6CkpERiwRHSxUpPousXNaSOLI8wQUhzwCoBhYSEICkpCQ4ODkhMTETv3r3Rr18/pKSkoKysDJs3b5Z0nKSVMhpvJtH1ixpSh0aYIESyWF2Cu379OgIDA7F+/Xr4+/tDQUEBfn5+iIqKgoWFBe7cuSPpOAkhhMgYVgmovLwcRkZGAABzc3NkZGRw61xcXGg+ICIxb16U4M0LusxLiCxilYC0tbXx7NkzAIChoSGysrJQVlYGAFBVVUVOTo7kIiSt2r0t13BvyzVph0EIkQBWCcjFxQXh4eGIjo6GkpISTE1NsXLlSty7dw/R0dHQ1taWdJyEEEJkDKsE5OPjgxEjRnAfQvXx8UFcXBzGjx+PS5cuYcaMGZKMkRBCiAxi1QuuTZs2WLFiBfe9jY0Njh8/jvv378PY2BgmJiYSC5AQUkvUpIQ0CytpiVglIEGMjIy4HRMIIZInalJCmoWVtEQ0kxwhhBCpoARECCFEKhp8CY6QpmDqay3tEAghEkIJiDRryrpq0g5BrKgjASH/EZqAbty4Ua8VWVpaNjoYQmQddSQg5D9CE9CUKVPA4XC4o19zOBwA4BkNu64MAB48eCCpGEkr9uTw3doXQ6UbByFE/IQmoAsXLnBfZ2VlwcfHB7a2tpg8eTK6du2K3NxcHDx4EGfPnsXatWubJFjS+uQnP5d2CIQQCRGagLp168Z9HRYWhuHDhyMsLIxbpq2tDQsLC9TU1CAyMhK2traSjZQQQohMYdUN+8qVK3BxcRFY5+zsjL///lusQRFCCJF9rBJQdXU1d/Trj2VnZ0NOjh4nIoQQUj+sumEPHDgQ27dvh42NDdTU/usWW1hYiF27dsHKykpiAbYWorrnAjQ9NCFE9rBKQMHBwXBzc8OIESNgb28PTU1N5ObmIj4+HoqKili2bJmk45R5orrnAjQ9NCFE9rBKQDo6OoiLi0N4eDiSk5ORm5sLbW1tjB07FnPmzIGWlpak4yStVHudDtIOgRAiIaxHQujatSvPlAyENAUzP3oAiBBZxToBlZSUYM+ePbh06RJevnyJX3/9FfHx8Rg4cCCGDx8uyRgJIYTIIFbd154/f46xY8di//796NGjB4qLiwEAVVVVmDlzJs9Dq4QQQggbrM6Afv75Z6iqquL48eNQVFTEsWPHAAALFixARUUFIiIiYG9vL9FASeuUvOhU7Yur1AmDEFnD6gzoxo0b8PLyQocO/DeEhw8fjvT0dLEHRgghRLaxSkBycnI8A49+qKioiB5EJYQQUm+sMsewYcOwc+dOlJaWcss4HA5qamoQExNDUzEQQgipN1b3gBYsWICvv/4ajo6OsLGxAYfDwc6dO/Ho0SPk5ORw7wkRQgghbLE6A9LR0cHJkycxZswYXL9+HW3atMG9e/fQt29fHDt2DDo6Oqw3WFNTg23btmH48OHo168fJk6ciMuXLzf4AxBCCGmZWD8HpKamhqVLl2Lp0qWN2uDy5csRExMDf39/6Ovr4+jRo/juu++wa9cuWFtbN2rdhBBCWg5WCcjT0xOTJk3C6NGj+eoSExMxffp0VjOi5ubm4o8//kBAQAC8vb0BACNHjoSbmxt++eUXSkCEj+E4U2mHUG/yHDnczRPcM5QGlSXkP6wSUFJSEpKTk/Hvv/9i/vz5De71lp6ejqqqKjg5OXHL5OTk0LdvX9y6datB6ySyTXOQvrRDqLfX78qwLiFCYB0NKkvIf1hnkunTpyMmJgbffPMNioqKGrSxPn36YN++fXz3jNLS0mBgYNCgdRJCCGmZWCcgW1tbHDp0CEVFRfjyyy9x586dem+sU6dOsLKyQps2/514RUVF4c6dO5g0aVK910dkX15SJvKSMqUdBiFEAlh3QgAAAwMDxMTEYPHixfDw8MBPP/0EXV3dBm24oqICa9euxd69exEQEABbW1uB7VJSUhq0fnEt31Teawh+0LdOdXV1i6gT93qfxt6rrZvX/GNtbF1paSlSMoX/fxX1f+RTy4pDS/lbas2a2zEaOHCgyPp6JSAAUFJSwqZNm/Dbb78hODgYvXv3rndQ//77L4KCgvD69Wts3boVI0eOFNr2Ux9AlJSUlEYt35SE3bSuIy8v3yLqmls8LSlWVVVVmH1mIrRe1P+RTy3bWC3pb6m1aonHqMFj6Hh7e+O3337D8+fP67VcUlISpkyZAmNjY5w6dUpk8iGEECK7WJ0BpaWlCSwfOnQojh49iqSkJFYbe/v2LebPn4/Ro0cjNDSUfZSEEEJkjtAElJ2djU6dOqFdu3bIzs4WugIOh4PBgwez2tjJkyeRm5uLIUOG4Nq1azx1qqqq6Nu3L8uwCSGEtHRCE5CdnR0iIyMxePBg2NnZCR0Nm2EYcDgcVg+i1vWcW7BgAV/dgAEDcODAAbZxt0g5ZfkoKH8lsI4eUCSEtDZCE9C+ffvQq1cv7mtxCAkJQUhIiFjW1RIVlL/C8gsbBNbRA4qEkNZGaAL6cIoFmm6BSIvVav7hnwg/UcP/dG6vAW2VLk0cESGfJjQBbd26tV4r8vHxaXQwhJCGETX8z1L7AEpApFkSmoAOHz7MeiUcDocSECGNJOosBqD7hET2CE1A58+fb8o4CBHo7uaE2het4B6ZqLMYgO4TEtnT4AdR69y+fRvz5s0TRyyE8CnPeo3yrNfSDoMQIgGsHkQtKirCkiVL8PjxYzAMwy1nGAYlJSUSC44QQojsYnUGFBISgqSkJJibm6O8vBwGBgZwdXWFrq4u5OXl691hgRBCCGGVgK5fv47AwECsX78e/v7+UFBQgJ+fH6KiomBhYdGgqRkIIYS0bqwSUHl5OYyMjAAA5ubmyMjI4Na5uLggJiZGMtERQgiRWazuAWlra+PZs2cYPnw4DA0NkZWVhbKyMqioqEBVVRU5OTmSjpMQ0kD0kCpprlglIBcXF4SHh6NNmzZwd3eHqakpVq5cCQ8PD0RHR0NbW1vScZJWqouVnrRDaPHoIVXSXLFKQD4+PsjLy8OePXvg7u4OHx8fzJkzB7GxsWAYBsuXL5d0nKSVMhpvJu0QCCESwioBtWnTBitWrOC+t7GxQVxcHFJTU2FsbAwTE8nNxEgIIUQ21XtK7jrdu3dH9+7dxRkLIXzevKDnzAiRVawfRF29ejXu3LmDykrB41HFx8eLNTBCAODelv8/ceHX0o2DECJ+rBJQcHAwrly5gjFjxkBLS0vo5HSEEEIIW6wSUN2DqFOnTpV0PIQQQloJVg+i1tTUUFdrQgghYsUqATk7O+Ovv/6SdCyEEEJaEVaX4JYsWYKvvvoK06ZNQ//+/dG2bVu+NjQhHSEtD42SQKSJVQLas2cPMjIykJOTg+fPn/PV04yohLRMNEoCkSZWCWj//v0YP348li9fjjZtGvzoECH1ZuprLe0QCCESwiqbVFRUYOTIkZR8SJNT1lWTdghEgJyyfBSUvxJYR5fuCFusMsqgQYPwzz//wN7eXtLxtHii/jArqwU/xEtIS1NQ/grLL2wQWEeX7ghbrBJQUFAQZs2aBVVVVTg6OqJdu3Z8bbp27Sr24FoiUX+YQUNnNnE0Ld+Tw3drXwyVbhyt0YcdFN5rcHg6K9CPKSIOrKdjAIA1a9YgLCyMp45hGHA4HDx48ED80ZFWLz+Zv9MLaRqiOijQjykiDqwS0L59+yQdByGEkFaGVQK6ePEi3NzcYGxsLOl4CCGEtBKsRkI4e/Ysrl27JulYCCGEtCKsEtCkSZMQGRmJ169fSzoeQgghrQSrS3D6+vrQ1taGk5MTXF1doaOjA2VlZZ4248ePl0iAhBBCZBOrBDRnzhzu6z179vDVczgcSkBEItrrdJB2CIQQCWGVgNLS0iQdByECmfnRA0CEyKp6ja1TVlaGv//+G4WFhejYsSM+//xzqKioSCo2QgghMox1Atq7dy/Wr1+PiooKMAwDAGjfvj38/f0xbdo0iQVICCFENrFKQH/++Sd+/vln2NrawsPDAzo6OsjKykJ0dDRWrVoFAwMD2NnZSThU0holLzpV++IqPXlPiCRIc2BZVglo586dGDFiBLZt28Yt69GjB2xtbeHj44NNmzZRAiKEkBZImgPLsnoO6OHDhxg7dqzAui+++AKPHz8Wa1CEEEJkH6szIEVFRRQXFwusKy4uhqKiojhjavZoygVCCGk8VgnI2toaERERcHBwgKamJrc8Pz8fv/76K4YNGyaxAJsjmnKBEEIaj1UCCgwMxIQJE+Dk5ARbW1t069YNOTk5uHjxIhQUFLBkyRJJx0kIIUTGsLoHpK+vjyNHjsDZ2Rk3b95EVFQUbty4AUdHR/zxxx/o0oVmPySEEFI/rJ8D0tHRwerVqyUZCyF8DMeZSjsEQoiEsE5A1dXVePLkCV69Enzz3dLSUmxBEVJHc5C+tEMghEgIqwSUmpoKHx8fvHz5kjsKwodoSm5CCFvSfPCRNC+sEtCyZcugpKSE33//Hdra2uBwOJKOixAAQF5SZu0LGpO0xZDnyOFuXrrQ+srqSoRe3iqwTtIPPpLmhVUCun//PsLCwjBgwABJx0MIj6ex92pfBEo3DsLe63dlWJcQIbSeHlUgdVj1guvYsSMqK8XzgCXDMPj999/h4OAAU1NTODg4YP/+/WJZNyGEkJaD1RmQu7s7Dhw4ABcXF8jLyzdqg/v370dISAjc3d0xdOhQXLlyBcHBwZCTk4O7u3uj1i1ONNoBIYRIFqsEpKKiglevXmHUqFGws7ODuro6XxsfHx9WG/z111/h6OiI5cuXAwCcnZ1RWFiI7du3N6sERKMdEEKIZLFKQB9Owx0fH89Xz+FwWCWgzMxMZGdnY/78+Tzlzs7OiI+PR2ZmJvT1qdstIYS0BqwS0Pnz58WysYKCAgDgSzJ17wsKCigBEUJIK8FhBD3YIyHJycmYMmUKjh07ht69e3PL79+/Dzc3N+zbtw9WVlZNFQ4hhBApYtULTtLouSJCCGl9mjQB1fWgq6qq4imve9/YHnaEEEJajiZNQHWjZr98+ZKnPCsri6eeEEKI7GvSBKSvrw8tLS1cuHCBp/zChQvQ0tKiDgiEENKKNPk9IG9vbxw5cgRhYWE4f/481qxZgyNHjmDWrFli2waNttC8vHz5EiYmJnz/Vq5cCaD2Emx4eDiGDRsGU1NTjBo1CmfOnJFy1K3Hxo0bMXXqVJ4yNsekvLwcP//8MwYNGgRTU1N8+eWXSE5ObsrQWw1Bx+jGjRsC/64iIyO5bZr7MWI9HYO4TJ06FQzDIDo6GpGRkejUqRN+/PFHTJ48WWzbaCmjLbQWDx8+hIqKCsLDw3nKu3btCgBYv349du3aBW9vb5iamuL48ePw9fXFjh07YGNjI42QZV5lZSXy8vJw5swZ7NixA/379+epZ3NMlixZgvPnz2Pu3LnQ1dXFgQMHMH36dMTGxqJnz57S+Fgy5VPH6OHDhzAyMsKyZct4yo2MjLivm/0xYmSQjY0NM3v2bJ6yWbNmMTY2NlKKqHXbsWMHM3HiRIF1b968YUxNTZkVK1Zwy2pqapgvvviCcXd3b6oQW50VK1YwxsbG3H9Tpkzh1rE5Js+ePWOMjY2ZPXv2cNtUVFQwQ4YMYYKCgprug8gwUceIYRgmODiYCQgIELp8SzhGzaIbtjjVjbYwevRonnJnZ2dkZ2cjMzNTSpG1Xg8fPoSxsTEA8M0n9c8//6CyshKjRo3ilnE4HDg7O+P27dt4//59k8baWkybNg0xMTGIiYmBnZ0dTx2bY1J3GefDNoqKirCzs8ONGzea5DPIOlHHCAAePXok9O8KQIs4RjKXgNiMtkCa1sOHD1FaWgo3NzeYmZlh5MiRiIiIAMMwIo9XdXU1ioqKpBGyzNPR0YGFhQUsLCzQsWNHnjo2xyQ/Px/t2rWDlpYWTxs9PT36GxMTUccIqP27yszMhLOzM8zMzPB///d/iImJ4da3hGPU5PeAJK3umaJ27drxlCsqKvLUk6bBMAwyMjLw6tUrBAYGQkVFBSdPnsS6deugrKyM9u3bAwAUFBR4lqPjJT11Z52ijklVVRX3/YeUlJTorLUJFBUVoaioCKmpqZg/fz44HA4OHjyIH374AZ07d4a9vX2LOEYyl4CEodEWpKOqqgphYWH4/PPPub/i7OzsUFVVhc2bN2Px4sUCl6Pj1fywOSZ03JpG27ZtsW3bNlhbW3N/xI0YMQKTJk3Chg0bYG9vL3TZ5nSMZO4SHI220Ly0bdsWjo6OfJcQRo0aheLiYu5DyB//IqPjJT1t2tT+LhV1TNq0aSPw7LSqqoq7PJEcVVVVODo6cpMPUJtYRo4ciQcPHuD9+/ct4hjJXAKi0RaalydPniA6Oho1NTU85dXV1QAAAwMDAIKPl5ycnMBr30SyOnfuDED0MencuTPKy8vx6hXvpI0vXrzgLk8k5+7duzh69ChfeU1NDdq1awc5ObkWcYxkLgHRaAvNS15eHpYuXYrr16/zlJ8/fx7a2toYMWIE2rVrx3e8Ll68iH79+vHdyyOSZ2Fh8cljYmlpCQA8bd6/f4+EhAQa0b4JpKenY9GiRXj69ClP+fnz59G3b1/Iy8u3iGPUPM7DxMzb2xsrVqxAp06dMGDAAKSkpODIkSN8D2wRybO0tISlpSWCgoIwc+ZM6Ojo4OLFizh+/Di3I4KHhwciIiLQtm1bGBsb49y5c0hKSkJERIS0w2+V2BwTQ0NDODk5YeXKlSgrK0O3bt1w6NAhvHjxAps2bZLyJ5B9o0aNQkREBLy8vODt7Q0NDQ3ExcXh9u3b2Lt3L4CWcYyadD6gpsIwDKKiohAdHY3nz5+jU6dO8PLywtSpU5vVDbjWori4GBs3bsT58+dRVFSEHj16YMaMGXBxcQFQ+6ts27ZtOHHiBF6+fAkdHR3MmzcPY8aMkXLkrcPixYuRlZXF/eIC2B2TsrIyrF27FpcuXUJeXh4+++wzLF68GNbW1tL4GDJN0DHKzs7G2rVrkZiYiLKyMvTp0we+vr4YOnQot01zP0YymYAIIYQ0fzJ3D4gQQkjLQAmIEEKIVFACIoQQIhWUgAghhEgFJSBCCCFSQQmIEEKIVFACIoQQIhWUgAghhEgFJSBCPpKamootW7Y0ePktW7bAwcGB+97BwaFR6xO3xn4+tsrKymBnZ4d///1X4tsCgLdv38LOzg73799vku2RxqMERMhHxP0FHRYWBjc3N7Gtr7GaKgGtWbMGAwcORN++fSW+LaB2wry5c+ciODi4SbZHGo8SECESNmDAAOjp6Uk7jCaVnZ2NQ4cO4dtvv+WWjRo1Cr6+vnxt/f394ezszFP23Xffwc3NDXfv3sW0adNgYWEBOzs7nDhxAgBw69YteHp6ol+/fnBycsKVK1cAAGPHjsXTp09x+fJlCX46Ii6UgIhI79+/x6ZNm+Do6AgzMzNYWVlh3rx5KCws5LZxcHDAunXrsGrVKgwbNgxmZmb44osvEBsbW682bLdnYmKCs2fPYvfu3Rg5ciRWr17NrTtz5gy+/PJLmJmZYfjw4di0aRPPpFwODg6IiorC1q1bMXz4cJiamuKrr77iXrZxcHDgztJqYmKCqVOnitw/J06cwP/93//BzMwMI0aMwPbt2/nmPvr4EhzbfRoZGYlNmzZh2LBhMDc3h5eXF/Ly8nD48GE4OTnB3Nwcrq6ufF+2ovbBpz7fp/afqH3/oX379sHQ0BB9+vQBAFRWVuLZs2fo1asXX9sHDx6gZ8+ePGVpaWmoqKjA7Nmz0b9/fwQFBaG6uhqLFi1CbGws5s2bBysrKwQEBODNmzcICgrC27dv0a5dO4waNQq7d+8WfMBIsyKT0zEQ8QkODsbZs2cxd+5cGBgY4OnTp1i/fj3Ky8vx22+/cdsdOHAAmpqaWLBgAZSVlXHkyBEsXrwYlZWVcHd3Z92G7fZ27dqFnJwcTJ06lTv674kTJxAYGIjx48dj1qxZePDgAXbs2IHCwkIsX76cu+y+ffugoaGBgIAAKCgoYOvWrfDx8cFff/2FsLAwxMfHY8eOHdizZw/U1NSE7pu9e/fif//7H1xdXTF//nwUFxdj+/btKCkpgYqKSqP36c6dO2FgYIClS5ciPz8fYWFh8PT0BMMw+O6776CiooIdO3bAz88PV69ehYqKyif3gajPx3b/Cdr3H7t8+TJ3PhoAePToEaqrq/kS0Nu3b/Hs2TN88cUX3LLXr18jOzsb6urqOHLkCHR0dAAA6urqmD9/PtavX4/Y2FhoamoCqJ11Nzg4GJmZmTAxMYGlpSUOHTqEsrIykceBNAMMISI4OTkx+/bt4ynz9/dn7O3tue/t7e0ZS0tLpri4mKfdrFmzmCFDhjAVFRWs2rDdnrGxMWNpackUFRVxy8rLy5khQ4YwixYt4ln28OHDjImJCZORkcGN1cHBgXn37h23zY0bNxhjY2MmPT2du4yxsbHI/VJWVsYMHDiQCQwM5CkvKipiBgwYwLd/Nm/ezH3Pdp+OGTOGef/+Pbfshx9+YExMTJjMzExuWUpKCmNsbMz8/fffrPeBoM/HdllB+/5jJSUljImJCbN//35uWWxsLGNsbMy8ePGCp+0///zDGBsbM2fPnuWWJScnM8bGxsyuXbt42sbHxzPGxsZMdHQ0T3lcXBxjbGzMPHz4kGEYhnn06BFjbGzMnD9/XmiMpHmgS3BEpDNnzsDDwwPl5eW4f/8+Dh48iMTERL52o0eP5jtbmDRpEgoKCnDr1i3Wbdhuz9XVFRoaGtz3t27dQkFBAcaMGYPKykruPycnJ7Rp04Z7jwAA7OzseGZa1dbWBgC+qYtFSU1NRUlJCcaNG8dTrqGhgVGjRolclu1ntLe3R5s2/12k0NTURJcuXXjuJ2lpaQGovcRVn33wsfos+/G+/1hubi4YhuGZTj09PR2qqqrcs5k6Dx48AACeS3B1ZY6OjjxtMzIyBJY/efIEbdu25c523KlTJwD8U4qT5ocuwRGRLl++jPDwcNy7dw86Ojro3r07evXqhczMTJ52dV/igsqKiopYt2G7PV1dXZ73dcnDy8tL4OfIysrivlZXV+epa8gkhbm5uQD+SwAf6tatm8hl2X7Gj6cj53A4Asvq1GcffKw+y3687z9WWloKAGjfvj23LD09XeD9n9TUVCgrK/OsMy0tDerq6nwdNx48eIBu3bpxE8yH5UZGRtx9U3fZrbi4WGScRPooARGhMjIyMHv2bLi4uGDHjh3o0KEDAGDTpk18X5bZ2dl8y9d9SXft2pVVm/ps78MvN+C/RBYaGorPPvuMbzuifrE3RN32cnNz0aNHD566vLw8ocvV5zM2NKaG7IP6LPvxvv9YXYIvLy/nlqWlpcHOzo6v7dWrV9GzZ0+eRJqWlia0s4Kw8v79+3PfV1RUAABUVVVFxkmkjy7BEaHu37+P9+/fY8qUKdwvSgC4efMmmI8m0j19+jRKSkp4ymJjY6GgoAATExNWbeqzvY/16tULqqqqyM3NhYWFBfefgYEB1q1bx0104tKrVy+oq6vj4MGDPOUlJSX4888/hS7XmM/IJqaG7gNx7j8tLS3IycmhoKAAQO3ZVV5eHk8vP6D22D979ozn8hvDMHj06BFfoqmsrERGRgZ69+7NU15WVoasrCyeddSdTQs6OyXNC50BEaGMjY0BAJs3b4a7uztKS0tx4sQJpKamoqqqCrdu3cLnn38OoPaLY+LEiZg1axbU1dVx6tQpnDx5EgEBAdxfop9qU5/tfUxFRQVz587FmjVrkJeXh8GDB6OyshJ79uxBSUkJzMzMWH9uZWVlAEBcXBw+++wzmJqaCmwTGBiIpUuXws/PD87OzqiqqsIvv/zC1w27ofu0vtjuA0GfT9z7r2/fvtx7Oenp6QCAxMRE/PjjjzAxMcG9e/dw8eJFALU95K5duwZra2tkZmbizZs3fAno0aNHqKqq4itPS0sDwzB895A4HA5PLzzSPFECIkL17NkTy5cvx2+//QY/Pz9oa2tjwoQJ+Pbbb+Hj44Ndu3ZxvyynTJkCANiwYQMKCwuhq6uLNWvW8IwA8Kk29dmeINOnT0eXLl2wfft2/PHHH1BTU4O1tTUCAwOhpKTE+nMPGjQIpqamWLx4MQYMGIC9e/cKbOfu7g41NTVs2bIF8fHxUFdXx+jRo6GkpISTJ082ep82BJt9IOzziWv/AbUdBY4cOQKgNklwOBxs2LABq1atwrFjx2BhYYF9+/YhKCgIGRkZ3LO/tLQ0AOA706lLZh+X17X/MAGlpKSgb9++PJ0gSPPEYRp73k9aPQcHB3z55ZcCn3KvTxsiO4qLi2FnZ4e9e/fi4MGDSEhIwIULFyS+3aqqKtjY2GDx4sUYO3asxLdHGofOgAghYqeurg5PT0/s3r0bL168ENixQRJOnjwJNTU1jB49ukm2RxqHOiEQQiRi9uzZuH37NlJTU7n3viTp7du32LRpE0JDQ9G2bVuJb480Hl2CI4RIzPPnzzFixAisWrUK48ePl3Y4pJmhBEQIIUQq6BIcIYQQqaAERAghRCooARFCCJEKSkCEEEKkghIQIYQQqaAERAghRCooARFCCJEKSkCEEEKk4v8B1oH3OiJ77yEAAAAASUVORK5CYII=\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "fig3, ax = plot.area_weighted(dataset['diameters'], dataset['Area'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig3.savefig(\"area_weighted.png\", dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">  👉 ***When to use and not to use the area-weighted approach?***\n",
    ">\n",
    "> You **should not use** the area-weighted mean for the calibration of paleopiezometers or for the comparison of grain size populations, as this is a poorly optimised central tendency measure ([Lopez-Sanchez, 2020](https://doi.org/10.1016/j.jsg.2020.104042)). On the other hand, the area-weighted distribution is useful to visualize the coarser size range, since in number-weighted distributions these sizes are diluted but can represent a significant area or volume.\n",
    "\n",
    "## Normalized grain size distributions\n",
    "\n",
    "Normalized grain size distributions are representations of the entire grain population standardized using an average of the population, usually the arithmetic mean or the median. The advantage of normalized distribution is that it allows the comparison of grain size distribution with different average grain sizes. For example, to check whether two or more grain size distributions have similar shapes we can compare their standard deviations (SD) or their interquartile ranges (IQR). In this case, the method ``plot.normalized()`` display the distribution on a logarithmic scale and provides the SD or IQR of the normalized population depending on the chosen normalizing factor."
   ]
  },
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      "=======================================\nNormalized SD = 0.165\nKDE bandwidth =  0.04\n=======================================\n"
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "fig4, ax = plot.normalized(dataset['diameters'], avg='amean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig4.savefig(\"normalized_distrib.png\", dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's play by changing some of the function parameters. In this case, we are going to establish the median as an alternative normalization factor, and we are also going to smooth the kernel density estimator by increasing the value from 0.04 (estimated according to the Silverman rule) to 0.1. Also, we will set the appearance of the figure using the figsize parameter, where the values within the parentheses are the (width, height) in inches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "=======================================\nNormalized IQR = 0.221\nKDE bandwidth =  0.1\n=======================================\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(<Figure size 432x360 with 1 Axes>,\n",
       " <AxesSubplot:xlabel='normalized log grain size ($y / med_{y}$)', ylabel='density'>)"
      ]
     },
     "metadata": {},
     "execution_count": 12
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x360 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plot.normalized(dataset['diameters'], avg='median', bandwidth=0.1, figsize=(6, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that in this case, the method returns the normalized inter-quartile range (IQR) rather than the normalized standard deviation. Also, note that the kernel density estimate appears smoother resembling an almost perfect normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python394jvsc74a57bd0920d8a9a5f744292f67977cf6b2a478b3d0cc37b276fff2fc5e6f9a3f188c865",
   "display_name": "Python 3.9.4 64-bit ('base': conda)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}